Fourrooms-Random

Description

This dataset was generated sampling random actions from the action space.

Dataset Specs

Total Steps

1000070

Total Episodes

10174

Dataset Observation Space

Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': Text(1, 14, charset=                                                              ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''(),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdeeeffghijklmnnoopqrrssttuvwxyzz{}))

Dataset Action Space

Discrete(7)

Algorithm

RandomPolicy

Author

Omar G. Younis

Email

omar.g.younis@gmail.com

Code Permalink

https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation

Minari Version

0.4.3 (supported)

Download

minari download D4RL/minigrid/fourrooms-random-v0

Environment Specs

The following table rows correspond to the Gymnasium environment specifications used to generate the dataset. To read more about what each parameter means you can have a look at the Gymnasium documentation https://gymnasium.farama.org/api/registry/#gymnasium.envs.registration.EnvSpec

This environment can be recovered from the Minari dataset as follows:

import minari

dataset = minari.load_dataset('D4RL/minigrid/fourrooms-random-v0')
env  = dataset.recover_environment()

ID

MiniGrid-FourRooms-v0

Observation Space

Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function FourRoomsEnv._gen_mission at 0x7f109ab0b160>, None))

Action Space

Discrete(7)

entry_point

minigrid.envs:FourRoomsEnv

max_episode_steps

None

reward_threshold

None

nondeterministic

False

order_enforce

True

disable_env_checker

False

kwargs

{}

additional_wrappers

()

vector_entry_point

None

Evaluation Environment Specs

This dataset doesn’t contain an eval_env_spec attribute which means that the specs of the environment used for evaluation are the same as the specs of the environment used for creating the dataset. The following calls will return the same environment:

import minari

dataset = minari.load_dataset('D4RL/minigrid/fourrooms-random-v0')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec